Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecasting

2025-09-17

AI Summary

Existing time series forecasting methods largely neglect explicit distribution alignment between historical inputs and future targets, assuming techniques like contrastive learning offer limited benefits—despite the presence of inherent distribution shifts, particularly in the frequency domain. Method: We propose TimeAlign, a lightweight, plug-and-play framework that enforces explicit alignment of historical and future representations via an auxiliary reconstruction task. We theoretically establish that this alignment enhances mutual information between input and target representations. Contribution/Results: TimeAlign is model-agnostic, incurs negligible computational overhead, and achieves significant improvements over state-of-the-art methods across eight benchmark datasets, demonstrating both broad applicability and empirical effectiveness.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Search and Optimization: Distributed Search
• Planning, Routing, and Scheduling: Temporal Planning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Representation learning techniques like contrastive learning have long been explored in time series forecasting, mirroring their success in computer vision and natural language processing. Yet recent state-of-the-art (SOTA) forecasters seldom adopt these representation approaches because they have shown little performance advantage. We challenge this view and demonstrate that explicit representation alignment can supply critical information that bridges the distributional gap between input histories and future targets. To this end, we introduce TimeAlign, a lightweight, plug-and-play framework that learns auxiliary features via a simple reconstruction task and feeds them back to any base forecaster. Extensive experiments across eight benchmarks verify its superior performance. Further studies indicate that the gains arises primarily from correcting frequency mismatches between historical inputs and future outputs. We also provide a theoretical justification for the effectiveness of TimeAlign in increasing the mutual information between learned representations and predicted targets. As it is architecture-agnostic and incurs negligible overhead, TimeAlign can serve as a general alignment module for modern deep learning time-series forecasting systems. The code is available at https://github.com/TROUBADOUR000/TimeAlign.

Problem

• Bridging distributional gap between input histories and future targets
• Correcting frequency mismatches between historical inputs and outputs
• Increasing mutual information between representations and predicted targets

Innovation

• Lightweight plug-and-play alignment framework
• Reconstruction task learns auxiliary features
• Corrects frequency mismatches via representation alignment